Project Summary In the last few decades, the outcomes for breast cancer patients have improved dramatically with 10-year absolute risk of breast cancer death decreasing approx. 2% per year between 1990 and 2007. While great progress has been made in improving patient outcomes, there is still a large unmet need for improvement as the disease costs the US healthcare system approx. $20 billion annually, has an immense indirect impact on families and care-givers, and results in the death of 40,000 patients per year. Novel therapeutics have contributed to this improvement in outcomes but have been slow to entirely address this need as there are only a handful of targeted therapeutics that have been developed (e.g. trastuzumab, lapatinib, pertuzumab, ribociclib) which are costly (~$50,000). An alternative approach to improving outcomes for patients without requiring the discovery and development of new therapeutics is to improve the breast cancer prognosis as it has been shown that earlier detection of disease results in significantly improved clinical outcomes for patients. Currently, diagnosing breast cancer involves the usage of tissue biopsies (e.g. core needle) to remove patient tissue followed by histological sectioning and staining, and finally grading and staging of disease. While histopathology is the gold-standard for patient diagnosis today, it has a fundamental limitation as it only characterizes a small sub-sample of the overall biopsy. Biopsies are tissue volumes (0.84 - 2.1 mm diameter by 5 - 20 mm length) that are a sub-sample of a region of interest (i.e. tumor). The conventional histopathological approach involves sampling the region of interest by cutting a few hair-thin (3 to 5 m) 2D slices of the biopsy for characterization. Since a small portion (< 1%) of the tissue is analyzed, the analysis represents only a small cross-section of the total available data, and since many diseases are heterogenous, 2D histopathological analysis intrinsically yields an incomplete picture of the tissue. For example, this approach is limited as features such as the micro-metastases found commonly in the sentinel lymph nodes and dictating clinical outcomes can be entirely missed as they can be far smaller (0.2 to 2 mm) than the space between slices. It has also recently been shown that 3D histomorphological features of tissues can be accurate prognostic indicators of disease outcome and could potentially improve patient outcomes by being included in the histological workflow. Therefore, the focus of this project is to develop a tissue characterization platform that addresses this sub- sampling problem and allows for breast cancer biopsy tissues to be imaged in their entirety in 3D. Through this project, we will leverage our patented Visikol HISTO? tissue clearing technique to develop a high-throughput 3D breast cancer characterization platform. It will be demonstrated that this platform can be effectively used to easily generate hundreds of images from breast biopsy tissues and that pathologically relevant features can be digitally extracted from these tissues for automated analysis.